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Volume 13 Issue 4
Apr.  2026

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2026  >  13(4): 822-836
G. Lv, B. Wang, C. Xu, W. Ding, and J. Liu, “MFAINet: Multi-receptive field feature fusion with attention-integrated for polyp segmentation,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 4, pp. 822–836, Apr. 2026. doi: 10.1109/JAS.2025.125408
Citation: G. Lv, B. Wang, C. Xu, W. Ding, and J. Liu, “MFAINet: Multi-receptive field feature fusion with attention-integrated for polyp segmentation,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 4, pp. 822–836, Apr. 2026. doi: 10.1109/JAS.2025.125408
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MFAINet: Multi-Receptive Field Feature Fusion With Attention-Integrated for Polyp Segmentation

doi: 10.1109/JAS.2025.125408
Funds:  This work was supported by the Science and Technology Project of Gansu (22YF7GA003, 24JRRA864, 21YF5GA102, 21YF5GA006, 21ZD8RA008, 22ZD6GA029), the National Natural Science Foundation of China (62576178), the National Key Research and Development Program of China (2024YFE0202700), the Natural Science Foundation of Jiangsu Province (BK20231337), the Fundamental Research Funds for the Central Universities (lzujbky-2022-ct06), and Supercomputing Center of Lanzhou University
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    Abstract
  • Colorectal cancer has become a global public health concern. Removing polyps before they become malignant can effectively prevent the onset of colorectal cancer. Currently, multi-receptive field feature extraction and attention mechanisms have achieved significant success in polyp segmentation. However, how to effectively fuse these mechanisms and fully leverage their respective strengths remains an open problem. In this paper, we propose a polyp segmentation network, MFAINet. We design an attention-integrated multi-receptive field feature extraction module (AMFE), which uses layering and multiple weightings to fuse the multi-receptive field feature extraction and attention mechanisms, maximizing the extraction of both global and detailed information from the image. To ensure that the input to AMFE contains richer target feature information, we introduce a multi-layer progressive fusion module (MPF). MPF progressively merges features at each layer, fully integrating contextual information. Finally, we employ the selective fusion module (SFM) to combine the high-level features produced by AMFE, resulting in an accurate polyp segmentation map. To evaluate the learning and generalization capabilities of MFAINet, we conduct experiments on five widely-used public polyp datasets using four evaluation metrics. Notably, our model achieves the best results in nearly all cases.

     

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